Navigation/traffic control satellite mission study. Volume 3 - System concepts

Satellite network for air traffic control, solar flare warning, and collision avoidanc

Synthesis of new DPP-4 inhibitors based on a novel tricyclic scaffold

A novel molecular scaffold has been synthesized and its synthesis and incorporation into new analogues of biologically active molecules will be discussed. A comparison of the inhibitory activity of these compounds to the known type-2 diabetes compound (sitagliptin) against dipeptidyl peptidase-4 (DPP-4) will be shown

Hierarchically coupled ultradian oscillators generating robust circadian rhythms

Ensembles of mutually coupled ultradian cellular oscillators have been proposed by a number of authors to explain the generation of circadian rhythms in mammals. Most mathematical models using many coupled oscillators predict that the output period should vary as the square root of the number of participating units, thus being inconsistent with the well-established experimental result that ablation of substantial parts of the suprachiasmatic nuclei (SCN), the main circadian pacemaker in mammals, does not eliminate the overt circadian functions, which show no changes in the phases or periods of the rhythms. From these observations, we have developed a theoretical model that exhibits the robustness of the circadian clock to changes in the number of cells in the SCN, and that is readily adaptable to include the successful features of other known models of circadian regulation, such as the phase response curves and light resetting of the phase

Fast polynomial inversion for post quantum QC-MDPC cryptography

The NIST PQC standardization project evaluates multiple new designs for post-quantum Key Encapsulation Mechanisms (KEMs). Some of them present challenging tradeoffs between communication bandwidth and computational overheads. An interesting case is the set of QC-MDPC based KEMs. Here, schemes that use the Niederreiter framework require only half the communication bandwidth compared to schemes that use the McEliece framework. However, this requires costly polynomial inversion during the key generation, which is prohibitive when ephemeral keys are used. One example is BIKE, where the BIKE-1 variant uses McEliece and the BIKE-2 variant uses Niederreiter. This paper shows an optimized constant-time polynomial inversion method that makes the computation costs of BIKE-2 key generation tolerable. We report a speedup of 11.8x over the commonly used NTL library, and 55.5 over OpenSSL. We achieve additional speedups by leveraging the latest Intel\u27s Vector-PCLMULQDQ instructions on a laptop machine, 14.3x over NTL and 96.8x over OpenSSL. With this, BIKE-2 becomes a competitive variant of BIKE

Innovator resilience potential: A process perspective of individual resilience as influenced by innovation project termination

Innovation projects fail at an astonishing rate. Yet, the negative effects of innovation project failures on the team members of these projects have been largely neglected in research streams that deal with innovation project failures. After such setbacks, it is vital to maintain or even strengthen project members’ innovative capabilities for subsequent innovation projects. For this, the concept of resilience, i.e. project members’ potential to positively adjust (or even grow) after a setback such as an innovation project failure, is fundamental. We develop the second-order construct of innovator resilience potential, which consists of six components – self-efficacy, outcome expectancy, optimism, hope, self-esteem, and risk propensity – that are important for project members’ potential of innovative functioning in innovation projects subsequent to a failure. We illustrate our theoretical findings by means of a qualitative study of a terminated large-scale innovation project, and derive implications for research and management

Social Role in Organizational Management - Understanding People Behavior and Motivation

The aim of this work is to respond to the need to rethink the behavior and motiva-tion of employees in their relationship with managers and social groups, i.e., one`s main goal is based on increasing engagement in order to reach organiza-tional goals and job workers satisfaction, a complex concept that is influenced by different causes. Indeed, in this work it is analyzed the impact of working condi-tions on job satisfaction. This is where attention is drawn to the concept of entro-py, since we are not focusing on the value a variable can take, but on the effort that has been expended to obtain it. The idea of entropy comes from a principle of thermodynamics dealing with energy. It usually refers to the idea that everything in the universe eventually moves from order to disorder, and entropy is the meas-urement of that change, that is used here to understand and assess the workers behavior and motivation. The subsequent formal model is based on a set of logi-cal structures for knowledge representation and reasoning that conform to the above entropic view, then leading to an Artificial Neural Network approach to computation, an archetypal that considers the motive behind the action

Avoiding overfitting of multilayer perceptrons by training derivatives

Resistance to overfitting is observed for neural networks trained with extended backpropagation algorithm. In addition to target values, its cost function uses derivatives of those up to the $4^{\mathrm{th}}$ order. For common applications of neural networks, high order derivatives are not readily available, so simpler cases are considered: training network to approximate analytical function inside 2D and 5D domains and solving Poisson equation inside a 2D circle. For function approximation, the cost is a sum of squared differences between output and target as well as their derivatives with respect to the input. Differential equations are usually solved by putting a multilayer perceptron in place of unknown function and training its weights, so that equation holds within some margin of error. Commonly used cost is the equation's residual squared. Added terms are squared derivatives of said residual with respect to the independent variables. To investigate overfitting, the cost is minimized for points of regular grids with various spacing, and its root mean is compared with its value on much denser test set. Fully connected perceptrons with six hidden layers and $2\cdot10^{4}$, $1\cdot10^{6}$ and $5\cdot10^{6}$ weights in total are trained with Rprop until cost changes by less than 10% for last 1000 epochs, or when the $10000^{\mathrm{th}}$ epoch is reached. Training the network with $5\cdot10^{6}$ weights to represent simple 2D function using 10 points with 8 extra derivatives in each produces cost test to train ratio of $1.5$, whereas for classical backpropagation in comparable conditions this ratio is $2\cdot10^{4}$